用工作流图构建可落地的电商对话系统，兼顾灵活性与可控性
A Practical Approach for Building Production-Grade Conversational Agents with Workflow Graphs
- 通过工作流图结构管理对话逻辑，提升系统可解释性
- 在真实电商场景中实现高可靠性对话，满足业务约束要求
- 适合需要稳定落地的工业级对话系统开发者参考
大语言模型（LLMs）在搜索、推荐和聊天机器人等服务领域取得显著进展。然而，将前沿研究成果应用于工业场景面临挑战：既要保持灵活的对话能力，又要严格遵守服务特定约束，这因LLM的不确定性而产生冲突。本文提出一种实用方法，解决这一矛盾，并详细阐述应对其实际应用局限性的策略。以电商领域对话代理为案例，展示实施流程与优化手段。研究结果揭示了学术研究与真实应用之间的差距，提出了一个可扩展、可控制且可靠的AI驱动代理开发框架。
原文摘要 · Abstract (English)
The advancement of Large Language Models (LLMs) has led to significant improvements in various service domains, including search, recommendation, and chatbot applications. However, applying state-of-the-art (SOTA) research to industrial settings presents challenges, as it requires maintaining flexible conversational abilities while also strictly complying with service-specific constraints. This can be seen as two conflicting requirements due to the probabilistic nature of LLMs. In this paper, we propose our approach to addressing this challenge and detail the strategies we employed to overcome their inherent limitations in real-world applications. We conduct a practical case study of a conversational agent designed for the e-commerce domain, detailing our implementation workflow and optimizations. Our findings provide insights into bridging the gap between academic research and real-world application, introducing a framework for developing scalable, controllable, and reliable AI-driven agents.
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